Any arbitrary system of interest can have at least two states. The system of interest can be, for example, a human body. Typically, the system of interest either functions correctly i.e. a healthy state or has an error i.e. an unhealthy state. There may be several healthy and/or unhealthy states i.e. the number of the healthy and unhealthy states may be large. For example, a patient may still be healthy even if there exists some suggestion about possible progressive disease or the state of disease may vary from slight to serious. The state of the system of interest defines which healthy or unhealthy state the patient is in and how much the state of the system of interest differs from a control state specified beforehand. In medical applications the state of the system of interest defines the disease a patient has and how far the disease has advanced, as compared with the normal, healthy state.
Computerized methods are needed in the above-mentioned analyses of the systems of interest to efficiently utilize multidimensional data and to find complex relations in the data. Each dimension of the data relates to an aspect of the particular system that is being measured (i.e. an indicator) and from which measurement values (indicator values) are gathered. Typically, the computerized methods give only a classification (healthy/unhealthy) as an output. However, in many applications the computerized methods cannot make the final decision because of possible erroneous measurements and uncertainty in the data, or merely because the computer cannot fully mimic the knowledge and experience of an expert. In such cases, a human user such as a doctor needs to make the final decision.
Nowadays, there is typically a large number of biomedical data available to the user interpreting the state of a system of interest. For example, different biomedical signals and images measured and results of various tests i.e. measurements may be available for the user to inspect. Some of these values and pieces of information may have additional information on the normal range of the value, and the user needs to observe this range in addition to the value itself. The different measured biomedical values data may be at least partially conflicting, and the data may be heterogeneous so that combining the data heuristically or numerically may be difficult and unreliable. Determining the state of the system may therefore be very time-consuming and prone to errors in interpretation.
There is, therefore, a need for solutions that make it faster, easier and less prone to errors to infer a state of a system of interest from obtained biomedical measurement data.